2,232 research outputs found
Automatische Schätzung der Körperpose mit CNNs und LSTMs
In this thesis, we present an end-to-end approach to human pose estimation task that based on a deep hybrid architecture that combines convolutional neural network (CNNs) and recurrent neural networks (RNNs)
Investigating Semantic Alignment in Character Learning of Chinese as a Foreign Language: The Use and Effect of the Imagery Based Encoding Strategy
For learners of Chinese as a foreign language (CFL), character learning is frustrating. This research postulated that this difficulty may mainly come from a lack of semantic understanding of character-denoted meanings. Language theories support that when a learner’s semantic meaning increases, the orthographic structures that represent the underlying meanings also improve.
This study aimed to reveal CFL learners’ cognitive abilities and processes in visual-semantic learning of Chinese characters. Particularly, this study investigated the process by which English-speaking adolescent CFL learners, at the beginning to intermediate level, made mental images of character-denoted meanings to visually encode and retrieve character forms. Quantitative and qualitative data were gathered from image making questionnaires, writing, and reading tests, after learning characters in three commonly-used teaching methods (i.e., English, pictorial, and verbal). The data were analyzed based on a triangulation of the literature from Neuro-Semantic Language Learning Theory, scientific findings in cognitive psychology, and neuroscience.
The study found that participants’ semantic abilities to understand character-denoted meanings emerged, but were still restricted in familiar orthographic forms. The use of the imagery strategy as a semantic ability predicted better performances, most evidently in writing; however, the ability in using the imagery strategy to learn characters was still underdeveloped, and needed to be supported with sufficient contextual information. Implications and further research in visual-semantic learning and teaching characters were suggested
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
Using Neuroeducation as a Model to Evaluate the Effect of Imagery on Chinese Character Writing
Using imagery as a strategy for language learning may be helpful to encode linguistic forms into conceptual networks for long-term memory. Based on Arwood’s neuroeducation model of language learning, this research evaluated the effect of imagery in Chinese character writing by English-speaking adolescent students. After comparing imagery effects under three instructional conditions (i.e., English translation, pictorial presentation, and verbal-contextual interpretation), the results showed that the use of imagery predicted significantly better writing results in the immediate and one-week writing tests, but not in the four-week writing test. Cognitive analyses found that imagery was commonly used as a mediational strategy in the pictorial and verbal-contextual methods in the early learning phases. The pictorial method mainly elicited perceptual visual patterns which failed to support sustained memory. For a better character encoding and retrieval, images had to be generated associated with sufficient and relevant contextual information
Public Transit Arrival Prediction: a Seq2Seq RNN Approach
Arrival/Travel times for public transit exhibit variability on account of
factors like seasonality, dwell times at bus stops, traffic signals, travel
demand fluctuation etc. The developing world in particular is plagued by
additional factors like lack of lane discipline, excess vehicles, diverse modes
of transport and so on. This renders the bus arrival time prediction (BATP) to
be a challenging problem especially in the developing world. A novel
data-driven model based on recurrent neural networks (RNNs) is proposed for
BATP (in real-time) in the current work. The model intelligently incorporates
both spatial and temporal correlations in a unique (non-linear) fashion
distinct from existing approaches. In particular, we propose a Gated Recurrent
Unit (GRU) based Encoder-Decoder(ED) OR Seq2Seq RNN model (originally
introduced for language translation) for BATP. The geometry of the dynamic real
time BATP problem enables a nice fit with the Encoder-Decoder based RNN
structure. We feed relevant additional synchronized inputs (from previous
trips) at each step of the decoder (a feature classically unexplored in machine
translation applications). Further motivated from accurately modelling
congestion influences on travel time prediction, we additionally propose to use
a bidirectional layer at the decoder (something unexplored in other time-series
based ED application contexts). The effectiveness of the proposed algorithms is
demonstrated on real field data collected from challenging traffic conditions.
Our experiments indicate that the proposed method outperforms diverse existing
state-of-art data-driven approaches proposed for the same problem
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
Analysis of Marginal Discourse: Identity Construction in Campus Wall Graffiti
Campus wall graffiti plays a significant role in the identity construction of graffiti artists as a form of marginal discourse. However, due to its long-standing perception as a destructive activity and the unique cultural environment, graffiti research in China has been relatively underdeveloped, with a focus on theoretical investigations. This study collected a total of 391 textual and pictorial graffiti samples from a language university in China as the corpus. From the perspective of identity construction, this study analyzed the characteristics of campus wall graffiti, as well as the methods and content of identity construction. The findings revealed that campus graffiti exhibits characteristics such as informality, anonymity, and counter-culturality. The language behaviors and symbol usage in graffiti include both one-way output and two-way interaction, the employment of textual and pictorial symbols, and the use of metaphors and symbolism. The construction of identity in graffiti primarily involves cultural identity, social identity, and creative identity among others. Lastly, in terms of campus graffiti management, this paper proposes effective strategies from the aspects of discourse power allocation and management, as well as feedback and reconstruction of identity construction. These strategies aim to transform disorder into order and foster an open and inclusive campus culture
- …